I interact regularly with AWS to support our needs in MLOps and to some extent GenAI. 3 of the experts we talked to have all left for competitors in the last year.
re:Invent London this year presented nothing new of note on the GenAI front. The year before was full of promise on Bedrock.
Outside of AWS, I still can’t fathom how they haven’t integrated an AI assistant into Alexa yet either
[0]: https://www.aboutamazon.com/news/devices/new-alexa-generativ...
Only thing it can do is set a timer, turn off a light and play music.
It is still nice, but it’s so frustrating when a question pops into my mind, and I accidentally ask Alexa just to get reminded yet again how useless it is for everything but the most basic tasks.
And no, I won’t pay 240 dollars a year so that I can get a proper response to my random questions that I realistically have only about once a week.
By far the best thing currently available.
Grok has to be more than n-times (2x?) as good as anything else on the market to attain any sort of lead. Falling short of that, people will simply choose alternatives out of brand preference.
This might be the first case of a company having difficulty selling its product, even if it's a superior product, due to its leader being disliked. I'm not aware of any other instances of this.
Maybe if Musk switches to selling B2B and to the US government...
If you piss off half of your possible user base, adoption becomes incredibly difficult. This is why tech and business leaders should stay out of politics.
I think that's a wildly optimistic figure on your part.
Lets assume that developers are split almost 50/50 on politics.
Of that 50% that follows the politics you approve off, lets err on the side of your argument and assume that 50% of those actually care enough to change their purchases because of it.
Of the 25% we have left, lets once again err on the side of your argument and assume 50% care enough about the politics to disregard any technology superiority in favour of sticking to their political leanings.
Of the 12.5% left, how many do you think are going to say "well, let me get beaten by my competitors because I am taking a stand!", especially when the "beaten" means a comparative drop in income?
After all, after nazi-salute, mecha-hitler, etc blew up, by just how much did the demand for Teslas fall?
The fraction of the population that cares enough about these (on both sides) things are, thankfully, single-digit percentages. Maybe even less.
I had been saving up for a Tesla but now I am looking elsewhere. I think a lot of people are doing the same here in Canada. You can grok the actual numbers if you want.
Yeah but they don't stay out of politics, do they? Gemini painting black Nazis was a deliberate choice to troll the vast majority of the population who isn't woke extremists.
My family subscribes to Grok and it's because of politics, not in spite of it. The answer gap isn't large today but I support Musk's goal of building a truth seeking AI, and he is right about a lot of things in politics too. Grok might well fail financially, the current AI market is too competitive and the world probably doesn't need so many LLM companies. But it's good someone wants AI to say what's true and not merely what's popular in its training set.
And no, generic brand safety mishaps are not the same; everyone is not doing this.
oh the irony
Being able to just order something with zero shipping has a ton of value. I could drive down the street but it would still be an hour at the end of the day.
Video streaming has some value but there are a lot of options.
And it can't even do that without an Internet connection. As someone who experiences annoyingly frequent outages, it never ceases to boggle my mind that I have a $200 computer, with an 8" monitor and everything, that can't even understand "set a timer for 10 minutes" on its own.
But the project is pretty much dead, it was supposed to launch in February or March and is still not anywhere close to being out.
I'm curious if non prime members make up a big market for Alexa. I rarely use my smart devices for anything beyond lights, music, and occasional Q&A, and certainly can't see myself paying 20$/month for it.
Unless of course this is going to be met with a price hike for Prime...
* 2018: $99 to $119
* 2022: $119 to $139
We should expect a price hike from $139 to $159 in 2026, assuming the trend continues.
Hmmm... maybe I can install do this through a cheap tablet....
And I say, good. We need new, smaller companies with different cultures in this space. We don't want these giant corporations to dominate and control everything.
I argue it is both understandable (autonomy is a healthy thing) and also damages the culture at large.
Once a company gets big off its grand idea, there's little to no chance of it having another big winner, so buying one is best (and its cheaper too, you know it's a good idea, and you don't have to spend so much R&D on it.
You say this as if it's a coercive given, when you could just as easily say.. Nope, and continue to see how you compete with some agility. It might fail, but most of the big tech companies currently acquiring smaller companies themselves started small with acquisition offers being rejected along the way. Sure, there's selection bias at work there, but there are also many cases of smaller to mid-size companies that also said no to acquisition and still managed to find their successful niche.
Being acquired is not a given and neither is failure if you do compete in some way with the megacorps.
I see nothing about the current tech landscape that at all distinguishes it from previous landscapes in which smaller companies succeeded AND rejected acquisition.
It’s the same framing as calling offering someone a higher salary as “poaching” like we’re property being stolen by one lord from another.
Looking at you Steve Jobs and your anti poaching agreement
we need new, smaller companies with different cultures in every space but won’t be getting any in any space, especially not in this one
OpenAI has 500B valuation, Anthropic has more than 60B.
So essentially a lifestyle business - but some people do think they have growth potential.
Feel free to not leave this out, it's a pet peeve of mine. Thank you for the moment of catharsis.
People are so careful when writing anonymous HN comments and so careless in choosing where to invest their own money and the money of funds of which they are the professional manager
If Google's market cap were $25 trillion, practically nobody would buy Google stock (and practically everyone who already held the stock would immediately sell) because most investors do not believe that Google can ever pay enough dividends or buy back enough stock to justify such a high valuation.
A company's market cap is a collective estimate of how much money the company will to return to investors in the future. When the company is publicly-traded in an open informational regime such as the US, this collective estimate is usually quite "accurate" in the sense that it is very difficult for any single analyst or single team of analysts to improve on the estimate.
An investor can make a big bet on a small company, yes, but the market cap of a company is more than just an indication of how much money has been bet on the company: it also mean that every investor (big or small) who still holds the stock believes that the expected amount of money that company will return to shareholders exceeds the market cap: if there were a holder of Google stock that did not believe that, he would convert the shares into treasury bills or cash in the bank.
Of course, a lot of money invested in Google was invested at a much lower price; if everyone sold all at once you'd have a hard time finding 2.5T of new money to buy all those shares. We could argue about if "not selling" is the same as "choosing again at the new price" every day... but... Google's not the interesting case here anyway.
For a young company in a hot industry like OpenAI total market cap is even less relevant since so much of the company simply isn't liquid anyway and the numbers come from far fewer instances of purchases than for an established public one.
Amazon is turning into a dinosaur like Cisco or IBM.
Once a use case and platform has stabilized, they'll provide it via AWS, at which poiny the SME market will eat it up.
Just the training. Training off of the internet! Filled with extremists, made up nuttery, biased bs, dogma, a large portion of the internet is stupids talking to stupids.
Just look at all the gibberish scientific papers!
If you want a hallucination prone dataset, just train on the Internet.
Over the next few years, we'll see training on encyclopedias and other data sources from pre-Internet. And we'll see it done on increasingly cheaper hardware.
This tiny branch of computer sciences is decades old, and hasn't even taken off yet. There's plenty of chance for new players.
We already train on these encyclopedias, we've trained models on massive percentages of entire published book content.
None of this will be helpful either, it will be outdated and won't have modern findings, understandings. Nor will it help me diagnose a Windows Server 2019 and a DHCP issue or similar.
Just taking a look at python. How often does the AI know it's python 2.7 vs 3? You may think all the headers say /usr/bin/python3, but they don't. And code snippets don't.
How many coders have read something, then realised it wasn't applicable to their version of the language? My point is, we need to train with certainty, not with random gibberish off the net. We need curated data, to a degree, and even SO isn't curated enough.
And of course, that's even with good data, just not categorized enough.
So one way is to create realms of trust. Some data trusted more deeply, others less so. And we need more categorization of data, and yes, that reduces model complexity and therefore some capabilities.
But we keep aiming for that complexity, without caring about where the data comes from.
And this is where I think smaller companies will come in. The big boys are focusing in brute force. We need subtle.
By whom? Certainly no one I work with. AWS has some sharp edges and frustrations but we couldn't do half of what we do without it.
(Though I'm pretty familiar with some of the concepts, I know some things to avoid (e.g., "push this button to set up a very expensive global enterprise scale observability platform of numerous complicated services, because you asked about a very simple turn-key syslog service"), and I'm expecting the occasional configuration headache (and, lately, configuration wizard bugs).)
For a new startup, I'd use AWS for all serving and hosting purposes by default, iff you have someone who can avoid pitfalls, and handle problems.
If you don't have such a technical person, maybe start off with managed Kubernetes service with high-level UI, at AWS or one of the other cloud providers, and try not to make too big a mess (which might slow you down, or take you down) before you can afford to hire specialists to make sure it keeps working for you.
News to me
It's the same as saying buying electricity from a network is worse than having your own generators.
No matter who is funding that, they are going to be pushing hard for a return (ell, unless they like money going up in smoke)
Or ‘why every large public company tends to suck the same ways in the US eventually’
Since financial engineering is in many ways more essential than the actual business. His best example was a chain hotel. In the majority of cases, a typical hotel is a tax vehicle that happens to rent rooms. So no wonder everything becomes a bank. :)
The franchisee typically pays 10% to 20% royalty to the franchisor (the aforementioned companies). Otherwise, they rent hotel rooms and pay staff to clean them and rent them again.
What is the tax play? That the hotel owner can 1031 into bigger and better hotels? Anyone who owns real estate can do that.
Hotel owner (aka franchisee) puts in capital in a specific way under license, gets help operating it, in exchange for the 10-20% licensing fee paid back to the main corporation.
In many cases, the owner/operator is nearly turnkey, and it’s an effective way of setting up a defacto managed business investment, almost like a LP. Many of the franchised hotels are actually owned/operated by LPs setup for the purpose.
Also in many of these cases, the franchiser provides contacts for financing, may directly facilitate/recruit Capital, and may even provide loans to the franchisee directly.
For most of these larger hotels, the actual act of renting out rooms, etc. is pretty much all automated/managed through the central system anyway, and the majority of the operating costs are structured in such a way as to minimize tax liability.
Is it clearer now?
> a typical hotel is a tax vehicle that happens to rent rooms.
>In many cases, the owner/operator is nearly turnkey,
What does this even mean? Hotels can be turnkey, which in industry terminology means that everything is working sufficiently well such that you can start renting rooms immediately. An owner/operator being turnkey makes no sense.
> setting up a defacto managed business investment
Also makes no sense.
>Also in many of these cases, the franchiser provides contacts for financing, may directly facilitate/recruit Capital, and may even provide loans to the franchisee directly.
Even if true, what does this have to do with taxes?
>For most of these larger hotels, the actual act of renting out rooms, etc. is pretty much all automated/managed through the central system anyway,
No, the actual out of renting out rooms involves housekeepers, maintenance staff, guest service agents, cooks, and management making sure rooms are clean and habitable. Reserving a hotel room is mostly automated, but even that requires a person to manage conflicts of reservations (e.g. unexpectedly needing to extend a stay causing overbooking, changing room types, room locations, etc.)
>and the majority of the operating costs are structured in such a way as to minimize tax liability.
Who doesn't structure their operating costs to minimize their tax liability? If you file married joint instead of married separate or head of household, are you "structuring" your operating costs as a way to minimize tax liability?
The question of how a hotel is used to gain an tax advantage that would otherwise be unavailable remains unanswered.
And how is a hotel a mix of different asset types?
What does GPs and LPs have anything to do with using a hotel to gain a special tax advantage that is not available to any other commercial real estate?
How stocks and bonds come into play is beyond me, unless I am being trolled.
But to summarize, zero evidence of how a hotel is a “tax vehicle”, nor any clarification on what a tax vehicle even is, nor why any other business wouldn’t be able to use the same strategy (if it even exists).
Do some basic reading so you can ask informed questions from the answers you have already been given, instead of insisting someone is an idiot when they point out you are not asking useful questions.
And frankly, no one owes you these answers.
As far as I understand, becoming a bank is inviting a ton of overhead with little profit potential.
Which is the core premise of a bank, even if the business doesn’t say ‘Bank’ on the side of the building.
That these two “inevitable endpoint things” would happen to be linguistically closely related was unlikely.
It's a fun image, but just as Facebook isn't becoming Apple, and Amazon won't become OpenAI, evolution phenomenons are more complex than "everything becomes X"
The two strategies for plants are to grow super tall to absorb the sun, or super wide (and small) to.... absorb the sun.
Tall needs wood or other 'strong' polymer to support height. Short and wide is perhaps weak from an individual level but far more efficient.
And trees and grass respectively have such genetic diversity that it's clear that none of these damn plants are of the same genetic line.
Trees, on the other hand, are a growth habit, exhibited by species in a wide variety of plant families, even grasses (e.g palm trees).
We are all addicted to growth - everyone is chasing the hockey stick curve which means a business that provides a stable business and grows modestly is seen as a failure in some parts
It was common in the post wwii era in America and its Asian allies like Korea with its chaebols and Japan with its somethings I can’t remember the name of. The Asian countries forms were normally based around a single family, we’ll need more time with the current US form to see if they are also dynastic
As a bonus you will have a very long vacation.
We, the tech, are literally a leftover of the once overwhelming engineering superiority of the west that will shrink in the next 5 years.
It has never been in Amazon or Apple's DNA to chase a product that doesn't have clear revenue outcomes (as long as adoption lands). AI is no different.
IMO, it's the right decision for Amazon and wrong decision for Apple.
Apple, on the other hand, hasn’t even invested in any of the players.
Also, just yesterday, they appear to have raised $13B from actual investors, so it seems like they’re going to be fine.
https://www.anthropic.com/news/anthropic-raises-series-f-at-...
Their other problem is they value designers and product managers more than engineers (especially top tier AI engineers).
Both problems are basically the death knell of any hope for Apple to have good AI, but combined? It’s never gonna happen. Which is sad because Apple’s on-device hardware is quite good.
Truthfully, I don't think anyone would recommend their acquaintances to join Amazon right now.
That said, Amazon is actually winning the AI war. They're selling shovels (Bedrock) in the gold rush.
I'm no expert, but I'm pretty sure this[0] is what RTO 5 is.
[0] https://www.phoenixcontact.com/en-pc/products/bolt-connectio...
For senior in-demand talent you are not desperate, and really only desperate people go to work for AWS as they don’t have any better options at a company which respects their employees.
AWS is falling behind even in their most traditional area: renting compute capacity.
For example, I can't easily run models that need GPUs without launching classic EC2 instances. Fargate or Lambda _still_ don't support GPUs. Sagemaker Serverless exists but has some weird limits (like 10GB limit on Docker images).
> For example, I can't easily run models that need GPUs without launching classic EC2 instances.
Yeah okay, but you can run most entreprise-level models via Bedrock.
Fargate and lambda are fundamentally very different from EC2/nitro under the hood, with a very different risk profile in terms of security. The reason you can't run GPU workloads on top of fargate and lambda is because exposing physical 3rd-party hardware to untrusted customer code dramatically increases the startup and shutdown costs (ie: validating that the hardware is still functional, healthy, and hasn't been tampered with in any way). That means scrubbing takes a long time and you can't handle capacity surges as easily as you can with paravirtualized traditional compute workloads.
There are a lot of business-minded non-technical people running AWS, some of which are sure to be loudly complaining about this horrible loss of revenue... which simply lets you know that when push comes to shove, the right voices are still winning inside AWS (eg: the voices that put security above everything else, where it belongs).
How?
> The reason you can't run GPU workloads on top of fargate and lambda is because exposing physical 3rd-party hardware to untrusted customer code dramatically increases the startup and shutdown costs
This is BS. Both NVidia and AMD offer virtualization extensions. And even without that, they can simply power-cycle the GPUs after switching tenants.
Moreover, Fargate is used for long-running tasks, and it definitely can run on a regular Nitro stack. They absolutely can provide GPUs for them, but it likely requires a lot of internal work across teams to make it happen. So it doesn't happen.
I worked at AWS, in a team responsible for EC2 instance launching. So I know how it all works internally :)
No? You can reset GPUs with regular PCI-e commands.
> You can't really enforce limits, either. Even if you're able to tolerate that and sell customers on it, the security side is worse
Welp. AWS is already a totally insecure trash, it seems: https://aws.amazon.com/ec2/instance-types/g6e/ Good to know.
Not having GPUs on Fargate/Lambda is, at this point, just a sign of corporate impotence. They can't marshal internal teams to work together, so all they can do is a wrapper/router for AI models that a student can vibe-code in a month.
We're doing AI models for aerial imagery analysis, so we need to train and host very custom code. Right now, we have to use third-parties for that because AWS is way more expensive than the competition (e.g. https://lambda.ai/pricing ), _and_ it's harder to use. And yes, we spoke with the sales reps about private pricing offers.
"AWS Lambda for model running" would be another nice service.
The things that competitors already provide.
And this is not a weird nonsense requirement. It's something that a lot of serious AI companies now need. And the AWS is totally dropping the ball.
> AWS now has to take responsibility for building an AMI with the latest driver, because the driver must always be newer than whatever toolkit is used inside the container.
They already do that for Bedrock, Sagemaker, and other AI apps.
It seems that they just don't care about the high turnover.
1) High-quality training data is effectively exhausted. The next 10× scale model would need 10× more tokens than exist.
2) The Chinchilla rule. Hardware gets 2× cheaper every 18 mo, but model budgets rise 4× in that span. Every flagship LLM therefore costs 2× more than the last, while knock-off models appear years later for pennies. Benchmark gains shrink and regulation piles on. Net result: each new dollar on the next big LLM now buys far less payoff. The "wait-and-copy" option is getting cheaper every day.
But I agree with the following statement Matt Garman gave recently;
Amazon Web Services CEO Matt Garman said that using AI tools in place of junior employees was "one of the dumbest things I've ever heard" because these employees are "the least expensive" and "the most leaned into your AI tools."
Also, on average, Indian non-Tech employees who manages thousands of spreadsheets or manually manages your in-store cameras are much more cheaper than the "tokens" and the NVIDIA GPUs you can throw at the problem, at least for now and a foreseeable future.
I don't think his point was we should hire junior engineers because they're cheap and lean into AI and AI produces slop. His position is not that he wants to cheaply create slop.
He wants to hire people who are cheap and love using AI because he sees that as a better long term strategy than making senior engineers embrace AI late into their career.
Or more likely -- Amazon management knows just how hard writing actually is, how hard to produce something with clarity and signal instead of just common-knowledge cliches, and so they understand that this LLM wave is overhyped. They're letting the other big players do the hard work, and effectively selling LLMs short by abstaining from the race.
How do you explain the Elon keiretsu, though? Tesla and SpaceX are pretty tethered to the physical world, and in theory should have visibility into the same discrepancies that Apple sees. So why is Elon pushing so hard to develop Grok? Is it just ideology for him, or what?
And, despite all the haters, he does understand rocket science pretty well, and rocket economics even better.
> Shotwell had lunch with a co-worker who had just joined the then-startup company SpaceX. They walked by the cubicle of CEO Elon Musk. “I said, ‘Oh, Elon, nice to meet you. You really need a new business developer,’” Shotwell recalls. “It just popped out. I was bad. It was very rude.” Or just bold enough to capture Musk’s attention. He called her later that day in 2002 and recruited her to be vice president of business development, his seventh employee.
Can you imagine something like that working today?
He makes lots of unnecessary major and cringy mistakes in both engineering and business too, but his net on both counts is astounding.
And while he may overuse it for PR, he has put himself at great financial risk when pushing through major capability developments and business hurdles. His rewards were earned.
But the sick picture of the richest person in the world, spamming stupidity, and harming countless numbers of people's lives in order to prop up his juvenile ego is hard to look past for many. For good reason.
He is a strong mix of both extremes of capability/impact spectrum, not just one.
Amazon I think just hasn't understood how to cohesively integrate AI into their offerings. Meanwhile they're selling shovels to the prospectors with AWS.
I guess both of these understand the Ai moat is not very large, and don't buy into AGI dreams.
The most effective way to get an LLM to control a computer right now is to just give it a unix terminal because it's already a text-based environment where programs are expected to be highly interoperable.
What I'm saying is that you don't need to stop everything to redesign around AI, just allow for a decent level of interoperability that iOS (and largely android) doesn't currently have.
The mobile app development model is oriented around packaging somewhat useful software (that could usually be a web app) with malware and selling it for $0.99, necessitating a ton of sandboxing and preventing this type of interoperability in the first place. I would say focus on the semantic HTML aspect of the web and design some way for LLMs to interact with websites in an open-ended way.
I had an Amazon interview loop on the calendar during my recent job search, a couple of months back, but it was difficult to get excited; they think so very highly of themselves, for what they're offering - and I don't just mean the money, but the culture too. They treat you like an interchangeable wage slave, not like a respected professional; it's all hoops to jump through, and procedures to memorize - dance, monkey, dance!
The recruiter was shocked when I cancelled the rest of the interviews, like, aren't you even going to give us a chance? But no: I had received a good offer from an ambitious, well-organized, well-funded AI startup which was excited to have me on board. With that on the table, why would I put up with Amazon? They won't offer better pay, they can't offer a better culture, and they don't have more interesting problems to work on.
I don’t understand the complains about it. Amazon pays monthly cash ”sign-on bonus” in the first two years, which is ~ equal to the stock that you get in the years three and four (counting at the grant price). Is this fact not advertized well enough?
Maybe your friend talked about relocation bonus, which you need to pay back if you don’t work long enough.
Perhaps they recently changed their policies? I don't know, but it's not a risk I would want to take. Who would want to work for people who treated their coworkers like that?
The full payment that requires pro-rates is even worse. They expect you to pay it fully back. (ie. with the deducted taxes included!)
I bet it is possible to profit from a such scheme if Amazon is able to declare that as a reversed-transaction (similar to VAT-refunds) at the end of the fiscal year.
1. Relocation package a. Lump-sum (7k EUR): You get certain amount of money, and you deal with your own move yourself. (Albeit with some reimbursement possible for the initial trips) b. "Other" (I don't remember the name): More supportive option, good if you have family & furniture to move. They essentially pay everything for you. c. Important: The 7k EUR was subject to the tax, hence I got taxed at 55% (EU) due to having no tax residency at the moment (obviously). Nobody ever mentions this. But the re-payment is with the tax-included, ie. you are expected to pay 7k back! 2. Sign-on bonus: This splits into 2-year period a. 1st year: 50% of the total bonus, transferred to your bank account on your first work day. b. 2nd year: Each month, you get 1/12 of the remaining 50%, essentially something like ~4.18% each month on the second year. c. The 50%/50% ratio may depend on the team/role/location, I heard some of the L4s joined to the team got split of 40%/60% (ie less in the first year) for reasons unbeknownst to me.
Conditions are pretty simple, if you leave (for any reason), you must repay monthly-pro-rated amount that you haven't worked given the total period is 24-months. ie. In Luxembourg, probation is 6-months. (Until) at the end of the probation, Amazon can just fire you for no reason. In this case, since the 2nd year sign-on hasn't vested yet, nothing to pay from that, but you must pay 1/4th of your "relocation expenses" and full half of (ie untaxed full amount divided by 2) sign-on bonus you receive on your first day. (ie. 25% of the total sign-on bonus)
Firstly, I know someone (a Greek national) who left Amazon during his 12th Month. Amazon demanded total of 4k+ euros from the guy, citing he hasn't finished his 12th month, hence the first half of his relocation bonus plus the 1-month of pro-rated sign-on bonus, before tax. As far as I know, it was more or less equivalent to his monthly gross salary, and he paid in installments.
Secondly, I heard someone joined from non-EU country in 2023, and essentially got laid off. But because she was in probation and obviously worker rights are much stricter in EU, Amazon just declared her as a probation-failed case instead of layoff. (She also got laid off within last 2 weeks of her 6-months long probation). Since she only got the residence permit recently, not having more than a few months (when unemployed as a 3rd-country national), plus Amazon demanded money to be paid back. As far as I know she contacted an labour lawyer and they basically advised her to go back and not to pay anything back as it becomes an international matter. And the costs/fees for such is much higher than what would Amazon get it back, hence she did what was suggested. Although it obviously burns the bridges but in this case, Amazon started the fire first...
---
As a result, the practices applied here falls no short of what you can hear from the news. As the company has no heart or soul, people are just numbers in a balance-sheet...
(Still, though - why work for people who know they're going to treat you so badly you'll probably have to quit?)
There was no way in hell I was going to sell my house and uproot my life to work for Amazon. Then the recruiter after she kept talking suggests I interview for a “permanently remote” [1] “field by design” role at AWS ProServe. I thought sure why not?
The plan was always to make some money - I made over a quarter million more over 3.5 years than I could have made as an enterprise dev working in Atlanta - put AWS on my resume, gain some industry contacts and move on in four years.
I saw the writing on the wall shortly before my 3 year anniversary. I played the game well enough to get past my next vesting period and get my “bust your ass and try to work through your PIP or receive a $40K+ severance and ‘leave immediately’”.
I didn’t hesitate. I took the severance and already had two job offers lined up and had been waiting on the severance offer.
[1] They forced their “field by design” customer facing roles in the office at the end of last year. I would have left anyway before I ever went back into the office.
Source: I worked at AWS from 2020-2023.
I spoke to someone who is there now and when you get your yearly review, now you can choose between mostly cash vs mostly stock for your raise and most people choose mostly cash.
I make the same now as I did when I was at AWS and I much prefer my all cash comp over my less cash + RSUs when I was there.
It would have been what ever it takes where base + prorated signing bonus + RSUs would equal $200K taking into account the 5/15/40/40 RSU schedule.
I don't understand this. A friend was recently offered an insane pay package from Amazon (compared to another big-tech). The way I saw it, the Amazon pay package was more attractive than the alternative because of the back-loaded vesting schedule.
Basically they pay you out in cash for the first two years, then after that you have an option to keep working there. If the stock price goes down in the first two years, you got your guaranteed cash -- no risk (and it would be a good time to interview again). If the stock price goes up, you now have basically an option on extra exposure in the form of staying longer with highly valued RSUs, and now getting some high proportion of your pay in RSUs.
It just seems straight up better? If you want the stock instead of fungible cash, just buy it on the open market?
Oh, and if the stock actually goes up more than 15%, then regardless of your performance you won't get a raise because you've already exceeded band penetration.
90% of the folks there that I know that were good have left for elsewhere. Of the ones that didn’t most are on H1Bs and basically have no choice but to stay and deal with the toxic environment.
No mention of reputation for harsh/ruthless/backstabby management practices towards employees (including for tech white collar, not just biz and blue collar)?
Is that not a major factor? Or are they not aware of it? Or is mentioning it politically off-limits? Or is putting it in writing a big PR risk? Or is putting it in writing a big legal risk?
I know Amazon's reputation for treating employees poorly came up in multiple discussions at one university's big-name AI lab, for example. Not only do some people read the news, but people talk, in groups and privately.
Not big-name companies in general, but specific companies among them.
It seems to be about belief of culture taint risk (e.g., the way engineering is done, or the misaligned careerism or sharp-elbowedness that's promoted by the company). Though there's also sometimes a belief that particular large companies hire lots of people who aren't good (only, apparently, at LeetCode interviews).
I'm a bit sympathetic to those theories, though I personally don't rule out any individual. I think, say, all the FAANGs do also have individual people who are capable and well-intentioned, and haven't been permanently branded with whatever problematic culture of the company they're at.
(Though there was a time when I thought a person wouldn't have gone to one particular social media company unless they were either a sociopath or completely unaware of news in the real world, but it's more nuanced now. And there's currently an aggressively pro-fascism company that AFAICT never should've seemed like a good idea to anyone who wasn't evil or oblivious, though, I have to remember that they like to hire "impressionable children", and we now have tech track undergrads who haven't had time for anything but STEM classes and LeetCode since early teens, so they might be forgiven. I was recently considering denylisting anyone who'd gone to a different tech company, which had a well-known decades-long history of chronic underhandedness, but then I saw that a colleague who'd majorly helped me out once had finally gone there. Which is another lesson to myself not to generalize in ways unfair to the individual.)
I personally don’t ascribe corporate amorality (as opposed to immorality) to all who work for it and thus with narrow exceptions would blacklist someone for working at a company who, e.g., has a CEO I dislike, practices wage suppression, etc.
Perhaps working for American companies remotely will change that view, but it’s too much a hassle for me at the moment.
logistics in terms of hardware and software not necessary bleeding tech in giants club
Please…
AWS does a lot of bleeding edge stuff, many of which never make it to prod.
apparently this bleeding edge tech is basically a low tech in another FAANG company
sorry, they are not in the same league
but if its for producing AWS slop service, amazon win. I can give you that
Maybe compared to FAANG, but not compared to most corporate developer jobs out there.
They don't seem to give a shit. In the retail space their name means "low quality Chinese counterfeit products with fake reviews" and I've seen no effort on Amazon's part to counter that perception either.
As an ex-Amazonian, I hate seeing this corporate euphemism. We would be reminded yearly that compensation at Amazon was “peculiar”, when really it was just relatively low for FAANG. I would have preferred frank honesty, which I think would look like “we pay relatively low wages, for relatively good engineers, and the difference makes more money”
No! Really? With RTO? Unbelievable /s
To me, that's a pretty good explanation.
The world is crazy with AI right now, but when we see how DeepSeek became a major player at a fraction of the cost, and, according to Google researchers, without making theoretical breakthroughs. It looks foolish to be in this race, especially now that we are seeing diminishing returns. Waiting until things settle, learning from others attempts and designing your system not for top performance but for efficiency and profit seems like a sane strategy.
And it is not like Amazon is out of the AI game, they have what really matters: GPUs. This is a gold rush, and as the saying goes, they are more interested in selling pickaxes that finding gold.
Customer service bots? Maybe. Coding bots? I bet they use some internally. Their customers don’t really need them, or if the customer does, the customer can run it on their side.
In general these fall into the category of things humans cannot do at the scale and speed necessary to run SaaS companies.
Many of the things LLMs attempt to do are things people already do, slowly and relatively accurately. But until hallucinations are rare, slow expensive humans will typically need to be around. The AI booster’s strategy of ignoring/minimizing hallucinations or equivocating with human fallibility doesn’t work for businesses where reliability is important.
Note that ML algorithms are highly imperfect as well. Uber’s prices aren’t optimal. Google search surfaces tons of spam. But they are better than the baseline of no service exists.
Disagree re: DeepSeek theoretical breakthroughs, MLA and GRPO are pretty good and paved the way for others e.g. Kimi K2 uses MLA for a 1T MoE.
Pay no attention to the cracks that are showing. Nevermind the chill. Everything is fine.
Don't need to train the models to make money hosting them.
AWS enables thousands of other companies to run their business. Amazon has designed their own Graviton ARM CPUS and their own Trainium AI chips. You can access these through AWS for your business.
I think Amazon sees AI being used in AWS as a bigger money generator than designing new AI algorithms.
Disclaimer; I work for amzn, opinions my own.
https://aws.amazon.com/blogs/machine-learning/aws-and-mistra...
Amazon wants people to move away from Nvidia GPUs and to their own custom chips.
Companies like OpenAI and Anthropic are still incredibly risky investments especially because of the wild capital investments and complete lack of moat.
At least when Facebook was making OpenAI's revenue numbers off of 2 billion active users it was trapping people in a social network where there were real negative consequences to leaving. In the world of open source chatbots and VSClone forks there's zero friction to moving on to some other solution.
OpenAI is making $12 billion a year off of 700 million users [1], or around $17 per user annually. What other products that have no ad support perform that badly? And that's a company that is signing enterprise contracts with companies like Apple, not just some Spotify-like consumer service.
[1] This is almost the exact same user count that Facebook had when it turned its first profit.
That's a bit of a strange spin. Their ARPU is low because they are choosing not to monetize 95% of their users at all, and for now are just providing practically limitless free service.
But monetising those free users via ads will pretty obviously be both practical and lucrative.
And even if there is no technical moat, they seem to have a very solid mind share moat for consumer apps. It isn't enough for competitors to just catch up. They need to be significantly better to shift consumer habits.
(For APIs, I agree there is no moat. Switching is just so easy.)
i am hoping that a device local model would eventually be possible (may be a beefy home setup, and then an app that connects to your home on mobile devices for use on the go).
currently, hardware restrictions prevent this type of home setup (not to mention the open source/free models aren't quite there and difficulty for non-tech users to actually setup). However, i choose to believe the hardware issues will get solved, and it will merely be just time.
The software/model issue, on the other hand is harder to see solved. I pin my hopes onto deepseek, but may be meta or some other company will surprise me.
Apple products as an example have an excellent architecture for local AI. Extremely high-bandwidth RAM.
If you run an OSS model like gpt-oss on a Mac with 32GB of RAM it's already very similar to a cloud experience.
Either way, it's just an example model, plenty of others to choose from. The fact of the matter is that the base model MacBook Air currently comes with about half as much RAM as you need for a really really decent LLM model. The integrated graphics are fast/efficient and the RAM is fast. The AMD Ryzen platform is similarly well-suited.
(Apple actually tells you how much storage their local model takes up in the settings > general > storage if you're curious)
We can imagine that by 2030 your base model Grandma computer on sale in stores will have at least 32GB of high-bandwidth RAM to handle local AI workflows.
The two are effectively separate businesses with a completely separate customer base.
*Microsoft enters the conversation
https://www.theverge.com/2023/10/24/23930478/microsoft-ceo-s...
They became nearly irrelevant because of mobile and had to claw their way back. That is not faring well.
They eventually made it out and survived because of cloud and gaming, but it took what many people consider a major transformation of the company.
Don't let your personal bias about AI cloud the way you see the world.
No, I'm not. Bill gates famously missed it (and/or severely underestimated the need for internet on Windows PCs) in 1994/5.
Microsoft completely missed the internet, and had to play catchup throughout 1995-1998.
> They became nearly irrelevant because of mobile and had to claw their way back. That is not faring well.
That never happened. They were in no danger at any time. The historic stock price charts, if you care to look them up, would show that the mobile threat you think there was did not even put a blip on their stock price and/or their revenue.
I mean, their revenue never even blipped.
(1) Internet: Netscape came out in 1994, and the internet tidal wave memo was 1995 and internet explorer came out the same year. Windows was rewritten with a focus on the networking stack, with Windows NT coming out in 1993 before the web boom. The internet's value is based on network effects and while you are right that they weren't first to market, they embraced it quickly and if they hadn't it likely would have been disastrous.
(2) Stock price: if you bought MSFT in October the year the iphone came out in 2007, you would take 6 years to break even. If you bought at the top in 2000 you wouldn't break even until 2016. This is a company that was limping along. During the mobile phone boom you'd have been better off putting your money in treasuries than in MSFT.
Yes they survived and were able to do well later. But my original point still stands: if you were running MSFT and wanted to be successful you would have embraced the internet and mobile. Deliberately sitting out a major technological innovation is not a recipe for success because the risk of ruin is very high. And the risk of becoming IBM is even higher.
Using equity returns to claim a business is limping along is bizarre. They were earning $10B profit per year in the early 2000s with 20%+ profit margins, something most businesses can only dream of doing, even today.
https://www.helgilibrary.com/charts/microsoft-corporation-pr...
If that business is limping along, then pretty much all other businesses are on life support.
at that given point in time, this was not their main businesses and they fared quite well.
microsoft missing the mobile is different, because mobile being a competitor to desktop destroyed microsoft's main business.
In all of these cases, the problem was losing track of what actually benefits users. AI has that problem really bad now because the infrastructure is expensive and the executive class has been sold on the idea that mass layoffs are just around the corner, and they’re pushing hard to ship before the benefits are there.
I and a few others still remember the site fondly, and it had the best UX of any social media service I've used since.
https://www.cnbc.com/2023/11/17/amazon-cuts-several-hundred-...
Of course not being able to monetise Alexa has always been a problem, but these and the article's issues are all to do with poor planning and top tier business direction.
While they're protected now, https://news.ycombinator.com/item?id=20980557 quotes the one I recall...
- Nobody has figured out how to make money from AI/ML other than by selling you a pile of compute and storage for your AI/ML misadventures.
https://www.cnbc.com/2025/08/08/chatgpt-gpt-5-openai-altman-...
> Last year, OpenAI expected about $5 billion in losses on $3.7 billion in revenue. OpenAI’s annual recurring revenue is now on track to pass $20 billion this year, but the company is still losing money.
> “As long as we’re on this very distinct curve of the model getting better and better, I think the rational thing to do is to just be willing to run the loss for quite a while,” Altman told CNBC’s “Squawk Box” in an interview Friday following the release of GPT-5.
Selling compute for less than it cost you will have as much revenue as you want to pay for.
could have said the same thing about most FAANG companies at one point or another.
Google doesn’t have this problem. They only run Google ads in their search results. Same thing for Facebook.
Paraphrase is from the podcast he was in with the stripe founder, cheeky pints I think
If I switch from Gemini Pro to Opus, that is good for Anthropic. If I switch from Opus 4 to 4.1, that’s not as good for Anthropic.
Sad that these CEOs can get away with this level of sophistry.
This is clearly not true. Google Ads? Every recommender system? Waymo self-driving? Uber routing algorithms?
If you swapped out ML for LLMs I would largely agree.
2019 was a different time - though I suspect that your statement about making money (as in profit) rather than just revenue (reselling compute for less than you bought it) would hold true for most companies.
And would this be admitting defeat to the powers of Terrible Orange Website to get you to write more?
As a side, in 2019 about a week after your tweets I was at a training session for Rancher which worked a reference to one of them into a joke.
Why buy the cow if you can get the milk for free?
Of course, the AI talent war may end up being an expensive and misguided strategy, stoked by hype and investor over-exuberance.
Don't really agree here, yes they screw you financially on cross-AZ bandwidth, but all of their popular services are built to work well across availability zones.
Most people don't need access to a low level consensus service like Paxos, instead they will be using one of amazons managed database services, or s3, that provides higher level abstractions, and automatically manages consensus behind the scenes.
Because Amazon will build services on top of the technologies that come out.
Just today on hn there was a guy that trained his tiny model and got better results than most of the big models. He wasn’t paid 200m.
The gold rush is here, but the results are still shaking out.
Meanwhile, the models are getting larger and more complex, with more users, putting the support infrastructure well beyond what individuals and even small companies can afford to outright buy. You can easily spend well over a million on even basic infrastructure to try to support some of the newer models and make it available to a few end users.
As a point of strategy for individuals and small entities, it really is cheaper in this case to spin up some AWS instances for a bit to do some LLM work and then spin them down when not in use.
So if you were AWS do you mine for gold? Or do you sell shovels?
The physical server itself would be the wooden handle, I guess.
We saw this with crypto mining where truckloads of expensive GPUs were dumped in the trash after the proof of work became so hard it became not worth the cost of electricity to keep on that generation of card.
AWS, Azure, GCP weren’t just renting servers. They built whole platforms - databases, ML stacks, dev tools, security. Way more than shovels.
The moat was owning the stack. MS used Azure to power Office and now Copilot. Google used infra to juice Search, YouTube, Ads. Even Amazon used it for retail + Alexa. They were mining gold and selling shovels.
And raw compute was never where the money was. Renting VMs was the cheap layer. The profits came from all the higher level services built on top.
Now with AI it’s even more obvious:
Models drive the workloads. OpenAI/Anthropic/DeepMind aren’t just customers, they’re shaping the infra itself. Whoever owns the models sets the rules.
No models = no moat. If AWS isn’t building frontier models, it’s just reselling Nvidia GPUs while MS + Google wrap their clouds around first party models + SDKs. That pulls customers deeper into their stacks, not Amazon’s.
Falling behind compounds. Training/deploying models forces infra breakthroughs (chips, compilers, scaling). If AWS isn’t in that game, they’ll eventually struggle to even run other ppl’s models as well as rivals.
So if Amazon “sits this one out,” it’s not just losing bragging rights. It’s giving up control of the future of compute.
I’m not 100% convinced this is true. Additionally, I’m not convinced that a waiting pattern right now sets Amazon up for a point of no return. It seems plausible for Amazon to pull an Apple here, to wait until technology is more mature and use their unique position to provide a quality offering.
Not a whole lot in their portfolio actually has a lot of Amazon technology behind it. They've got some mild forks here and there, and they've got some stuff like Fargate that has AWS R&D work behind it but piggybacks concepts/tech stacks that definitely didn't originate from Amazon.
A lot of their value has really nothing to do with developing the underlying technology.
But I think you are making it sound like Amazon's moat is that it came up with its own technology behind its services.
A lot of times AWS was just grabbing a bunch of popular open source stuff off the shelf and hosting it (e.g., RDS, EKS, etc). Yes there is some R&D work but almost none of what Amazon has come up with is rooted in their own work.
The value they give you is the hosting, maintenance, and compliance of all these services. If you're paying AWS extra to host your database on RDS or your Kubernetes cluster in EKS, you're generally not paying AWS to come up with a better database than anyone else, you're just paying them to help you manage permissions, backups, replication, and other maintenance/compliance/management issues that a company needs for its internal services.
In other words, Amazon's AI customers don't need Amazon to build models. They just need Amazon to use someone else's models, host them on private enterprise compute that easily ties in to existing infrastructure, RBAC, etc, and make everything compliant and easy to maintain. A whole lot of the value is being able to answer audits with "AWS handles our database backups/data security/etc" rather than saying "we have a great ops team and here's all our proof that we handle our database backups/data security/etc properly."
I think it's actually explicitly Amazon's job to sit this one out, especially since they never successfully made a good business or consumer ecosystem device like a smartphone or PC operating system.
See, that’s the problem with what Amazon has done to you. It’s always about money with you guys. Good research is about the opposite of money. The people who don’t know what that means, who can’t fathom to understand what “the opposite of money” means without turning everything into a contrived story about money: they can’t do good R&D. Every single great R&D director will tell you this, and a bunch of people will downvote this comment, who have never been in a meaningful R&D role.
A good research culture is capable of listening to broad, generalized, completely accurate criticism in public and not downvote. Downvoting is your problem guys!
OpenAI has a million little haters out there and do you know how much time their people spend downvoting comments online? Zero. And honestly they’re paid way better than the poor souls who have wound up at Amazon, so it’s really, truly the case that none of this money money money culture really adds up to much for the little guy.
If there’s any one person to point the finger at - like why does Amazon, with its vast resources and tremendous talent, produce basically zero meaningful publicly influential research - it’s Jeff Bezos. You’re talking about strategy? The guy in charge is a colossal piece of shit, with a piece of shit girlfriend and a piece of shit world view, at least as bad as Larry Ellison, whose only redeeming factor is that MacKenzie Scott is a much smarter person than he ever was.
It’s no surprise that AWS’s revenue growth is lagging behind GCP and Azure.
Beyond the AI talent gap, Amazon seems to be making serious missteps in its own core business.
It reminds me of Apple. At first, people thought Apple was being strategic by staying out of the AI race and waiting to pick the winner. But in reality, it turned out to be an inability to adapt to the new trend. I expect the same pattern from Amazon.
My intuition is that the root cause it's their frugal culture (frugal as in cheap). They don't want to start a compensation race.
Jassy’s long rambling answer on the last earnings call though does suggest that being way behind on AI is a sore spot for leadership.
Attracting top talent though is a challenge for Amazon beyond just AI. Their reputation has become a real issue and the top folks simply have better options.
Has anyone had a chance to use Kiro at all? At this point I'm not even interested in it anymore, even if I got an invite.
Also Amazon is in another capital intensive business. Retail. Spending billions on dubious AWS moonshots vs just buying more widgets and placing them across the houses of US customers for even faster deliveries does not make sense.
This is not really true. Google has all the compute but in many dimensions they lag behind GPT-5 class (catching up, but it has not been a given).
Amazon itself did try to train a model (so did Meta) and had limited success.
It is. It's wild to me that all these VCs pouring money into AI companies don't know what a value-chain is.
Tokens are the bottom of the value-chain; it's where the lowest margins exist because the product at that level is a widely available commodity.
I wrote about this already (shameless plug: https://www.rundata.co.za/blog/index.html?the-ai-value-chain )
Outside of computer, "the moat" is also data to train on. That's an even wider moat. Now, google has all the data. Data no one else has or ever will have. If anything, I'd expect them to outclass everyone by a fat margin. I think we're seeing that on video however.
Yeah, Google totally has a moat. Them saying that they have no moat doesn't magically make that moat go away.
They also own the entire vertical which none of the competitors do - all their competitors have to buy compute from someone who makes a profit just on compute (Nvidia, for example). Google owns the entire vertical, from silicon to end-user.
It would be crazy if they can't make this work.
Google theoretically has reddit access. I wonder if they have sort of an internet archive - data unpolutted by LLMs
On a side note, funny how all the companies seem to train on book archivr which they just downloaded from the internet
And privacy policies that are actually limiting what information gets used in what.
Tin foil hat time:
- If you were a God and you wanted to create an ideal situation for the arrival of AI
- It would make sense to precede it with a social media phenomena that introduces mass scale normalization of sharing of personal information
Yes, that would be ideal …
People can’t stop sharing and creating data on anything, for awhile now. It’s a perfect situation for AI as an independent, uncontrollable force.
Garbage in. Garbage out.
There has never been a better time to produce an AI that mimics a racist uneducated teenager.
I tend personally to stick with ChatGPT most of the time, but only because I prefer the "tone" of the thing somehow. If you forced me to move to Gemini tomorrow I wouldn't be particularly upset.
Gemini holds indeed the top spot, but I feel you framed your response quite well: they are all broadly comparable. The difference in the synthetic benchmark from the top spot and the 20th spot was something like 57 points on a scale of 0-1500
xAI seems to be the exception, not the rule
I don't know what you are talking about. I use Gemini on a daily basis and I honestly can't tell a difference.
We are at a point where training corpus and hallucinations makes more of a difference than "model class".
Right now the delay for Google's AI coding assistant is high enough for humans to context switch and do something else while waiting. Particularly since one of the main features of AI code assistants is rapid iteration.
Are you saying the only reason Meta is behind everyone else is compute????
I wouldn't be surprised if the likes of Anthropic wasn't paying AWS for its compute.
As the saying goes, the ones who got rich from the gold rush were the ones selling shovels.
It’s not just compute. That has mostly plateaued. What matters now is quality of data and what type of experiments to run, which environments to build.
However I do think you are missing an important aspect - and that's people who properly understand important solvable problems.
ie I see quite a bit "we will solve this x, with AI' from startup's that don't fundamentally understand x.
You usually see this from startup techbro CEOs understand neither x nor AI. Those people are already replacable by AI today. The kind of people who think they can query ChatGPT once with "How to create a cutting edge model" and make millions. But when you go in on the deep end, there are very few people who still have enough tech knowledge to compete with your average modern LLM. And even the Math Olympiad gold medalists high-flyers at DeepSeek are about to have a run for their money with the next generation. Current AI engineers will shift more and more towards senior architecture and PM roles, because those will be the only ones that matter. But PM and architecture is already something that you could replace today.
It still is! Lots of vertical productivity data that would be expensive to acquire manually via humans will be captured by building vertical AI products. Think lawyers, doctors, engineers.
As more opens up in OSS and academic space, their knowledge and experience will either be shared, rediscovered, or become obsolete.
Also many of the people are coasting on one or two key discoveries by a handful of people years ago. When Zuck figures this out he gonna be so mad.
Does it? Then how come Meta hasn't been able to release a SOTA model? It's not for a lack of trying. Or compute. And it's not like DeepSeek had access to vastly more compute than other Chinese AI companies. Alibaba and Baidu have been working on AI for a long time and have way more money and compute, but they haven't been able to do what DeepSeek did.
Are we living in the same universe? LLAMA is universally recognized as one of the worst and least successful model releases. I am almost certain you haven't ever tried a LLAMA chat, because, by the beard of Thor, it's the worst experience anyone could ever had, with any LLM.
LLAMA 4 (behemoth, whatever, whatever) is an absolute steaming pile of trash, not even close to ChatGPT 4o/4/5/, Gemini(any) and even not even close to cheaper ones like DeepSeek. And to think Meta pirated torrents to train it...
What a bunch of criminal losers and what a bunch of waste of money, time and compute. Oh, at least the Metaverse is a success...
https://www.pcgamer.com/gaming-industry/court-documents-show...
https://www.cnbc.com/2025/06/27/the-metaverse-as-we-knew-it-...
I recall Zuckerberg saying something about how there were early signs of AI "improving itself." I don't know what he was talking about but if he really believes that's true and that we're at the bottom of an exponential curve then Meta's rabid hiring and datacenter buildout makes sense.
Hopefully some big players, like FB bankrupt themselves.
1) LLMs as simple "next token predictors" so they just mimicry thinking: But can it be argued that current models operate on layers of multiple depth and are able to actually understand by building concepts and making connections on abstract levels? Also, don't we all mimicry?
2) Grounding problem: Yes, models build their world models on text data, but we have models operating on non-textual data already, so this appears to be a technical obstacle rather than fundamental.
3) Lack of World Model. But can anyone really claim they have a coherent model of reality? There are flat-earthers, yet I still wouldn't deny them having AGI. People hallucinate and make mistakes all the time. I'd argue hallucinations is in fact the sign of an emerging intelligence.
4) Fixed learning data sets. Looks like this is now being actively solved with self-improving models?
I just couldn't find a strong argument supporting this claim. What am I missing?
This line means, and literally says, that everything that follows is a summary or direct quotation from an LLM's output.
There's a more charitable but unintuitive interpretation, in which "commenting on them briefly" is intended to mean "I will comment on them briefly:". But this isn't a natural interpretation. It's one I could be expected to reach only after seeing your statement that 'none of the above is AI.' But even this more charitable interpretation actually contradicts your claim that it's not AI.
So now I'm even less sure I know what you meant to communicate. Either I'm missing something really obvious or the writing doesn't communicate what you intended.
Not sure what level of understanding are you referring to but having learned and researched about the pretty much all LLM internals I think this has led me exactly to the opposite line of thinking. To me it's unbelievable what we have today.
I can throw wide ranging problems at things like gpt5 and get what seem like dramatically better answers than if I asked a random person. The amount of common sense is so far beyond what we had it’s hard to express. It used to be always pointed out that the things we had were below basic insect level. Now I have something that can research a charity, find grants and make coherent arguments for them, read matrix specs and debug error messages, and understand sarcasm.
To me, it’s clear that agi is here. But then what I always pictured from it may be very different to you. What’s your image of it?
This, to me at least, seems like an important ingredient to satisfying a practical definition / implementation of AGI.
Another might be curiosity, and I think perhaps also agency.
What we are saying is that LLM's can't become AGI. I don't know what AGI will look like, but it won't look like an LLM.
There is a difference between being able to melt iron and being able to melt tungsten.
If I had to pick a name, I'd probably describe ChatGPT & co as advanced proof of concepts for general purpose agents, rather than AGI.
People selling AI products are incentivized to push misleading definitions of AGI.
I give it a high-res photo of a kitchen and ask it to calculate the volume of a pot in the image.
However, even "dumb" people can often make judgements structures in a way that AI's cannot, it's just that many have such a bad knowledge-base that they cannot build the structures coherently whereas AI's succeed thanks to their knowledge.
I wouldn't be surprised if the top AI firms today spend an inordinate amount of time to build "manual" appendages into the LLM systems to cater to tasks such as debugging to uphold the facade that the system is really smart, while in reality it's mostly papering up a leaky model to avoid losing the enormous investments they need to stay alive with a hope that someone on their staff comes up a real solution to self-learning.
https://magazine.sebastianraschka.com/p/understanding-reason...
Hell, I’d even say we have AGI if you could emulate something like a hamster.
LLMs are way more impressive in certain ways than such a hypothetical AGI. But that has been true of computers for a long time. Computers have been much better at Chess than humans for decades. Dogs can’t do that. But that doesn’t mean that a chess engine is an AGI.
I would also say we have a special form of AGI if the AI can pass an extended Turing test. We’ve had chat bots that can fool a human for a minute for a long time. Doesn’t mean we had AGI. So time and knowledge was always a factor in a realistic Turing test. If an AGI can fool someone who knows how to properly probe an LLM, for a month or so, while solving a bunch of different real world tasks that require stable long term memory and planning, then I’d day we’re in AGI territory for language specifically. I think we have to distinguish between language AGI and multi-modal AGI. So this test wouldn’t prove what we could call “full” AGI.
These are some of the missing components for full AGI: - Being able to act as a stable agent with a stable personality over long timespans - Capable of dealing with uncertainties. Having a understanding of what it doesn’t know - One-shot learning, with long term retention, for a large number of things - Fully integrated multi-modality across sound, vision, and other inputs/outputs we may throw at it.
The last one is where we may be able to get at the root of the algorithm we’re missing. A blind person can learn to “see” by making clicks and using their ears to see. Animals can do similar “tricks”. I think this is where we truly see the full extent of the generality and adaptability of the biological brain. Imagine trying to make a robot that can exhibit this kind of adaptability. It doesn’t fit into the model we have for AI right now.
You could fund 1000+ projects with this kinds of money. This is not an effective capital allocation.
Of course it might be the case, but it's not a thing that should be expressed with such confidence.
It’s also pretty useless to talk about whether something is AGI without defining intelligence in the first place.
The fact that philosophy hasn't recognized and rejected this argument based on this speaks volumes of the quality of arguments accepted there.
(That doesn't mean LLMs are or will be AGI, its just this argument is tautological and meaningless)
I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.
If the answer is no, that shouldn't lead to a deist conclusion. It can just as easily lead to the conclusion that a non-deterministic Turing machine is required.
> I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.
Since the real world (including probabilistic and quantum phenomena) can be modeled with deterministic computation (a pseudorandom sequence is deterministic, yet simulates randomness), if we have a powerful enough computer we can simulate the brain to a sufficient degree to have it behave identically as the real thing.
The original 'Chinese Room' experiment describes a book of static rules of Chinese - which is probably not the way to go, and AI does not work like that. It's probabilistic in its training and evaluation.
What you are arguing is that constructing an artificial consciousness lies beyond our current computational ability(probably), and understanding of physics (possibly), but that does not rule out that we might solve these issues at some point, and there's no fundamental roadblock to artificial consciousness.
I've re-read the argument (https://en.wikipedia.org/wiki/Chinese_room) and I cannot help but conclude that Searle argues that 'understanding' is only something that humans can do, which means that real humans are special in some way a simulation of human-shaped atoms are not.
Which is an argument for the existence of the supernatural and deist thinking.
It is not meant as an ad hominem. If someone thinks our current computers can't emulate human thinking and draws the conclusion that therefore humans have special powers given to them by a deity, then that probably means that person is quite religious.
I'm not saying you personally believe that and therefore your arguments are invalid.
> Since the real world (including probabilistic and quantum phenomena) can be modeled with deterministic computation (a pseudorandom sequence is deterministic, yet simulates randomness), if we have a powerful enough computer we can simulate the brain to a sufficient degree to have it behave identically as the real thing.
The idea that a sufficiently complex pseudo-random number generator can emulate real-world non-determinism enough to fully simulate the human brain is quite an assumption. It could be true, but it's not something I would accept as a matter of fact.
> I've re-read the argument (https://en.wikipedia.org/wiki/Chinese_room) and I cannot help but conclude that Searle argues that 'understanding' is only something that humans can do, which means that real humans are special in some way a simulation of human-shaped atoms are not.
In that same Wikipedia article Searle denies he's arguing for that. And even if he did secretly believe that, it doesn't really matter, because we can draw our own conclusions.
Disregarding his arguments because you feel he holds a hidden agenda, isn't that itself an ad hominem?
(Also, I apologize for using two accounts, I'm not attempting to sock puppet)
>Searle argues that, without "understanding" (or "intentionality"), we cannot describe what the machine is doing as "thinking" and, since it does not think, it does not have a "mind" in the normal sense of the word.
This is the only sentence that seems to be pointing to what constitutes the specialness of humans, and the terms of 'understanding' and 'intentionality' are in air quotes so who knows? This sounds like the archetypical no true scotsman fallacy.
In mathematical analysis, if we conclude that the difference between 2 numbers is smaller than any arbitrary number we can pick, those 2 numbers must be the same. In engineering, we can reduce the claim to 'any difference large about to care about'
Likewise if the difference between a real human brain and an arbitrarily sophisticated Chinese Room brain is arbitrarily small, they are the same.
If our limited understanding of physics and engineering makes the practical difference not zero, this essentially becomes a bit of a somewhat magical 'superscience' argument claiming we can't simulate the real world to a good enough resolution that the meaningful differences between our 'consciousness simulator' and the thing itself disappear - which is an extraordinary claim.
They're in the "Complete Argument" section of the article.
> This sounds like the archetypical no true scotsman fallacy.
I get what you're trying to say, but he is not arguing only a true Scotsman is capable of thought. He is arguing that our current machines lack the required "causal powers" for thought. Powers that he doesn't prescribe to only a true Scotsman, though maybe we should try adding bagpipes to our AI just to be sure...
He argues that computer programs only manipulate symbols and thus have no semantic understanding.
But that's not true - many programs, like compilers that existed back when the argument was made, had semantic understanding of the code (in a limited way, but they did have some understanding about what the program did).
LLMs in contrast have a very rich semantic understanding of the text they parse - their tensor representations encode a lot about each token, or you can just ask them about anything - they might not be human level at reading subtext, but they're not horrible either.
When it makes a mistake, did it just have a too limited understanding or did it simply not get lucky with its prediction of the next word? Is there even a difference between the two?
I would like to agree with you that there's no special "causal power" that Turing machines can't emulate. But I remain skeptical, not out of chauvinism, but out of caution. Because I think it's dangerous to assume an AI understands a problem simply because it said the right words.
Regardless of whether Searle is right or wrong, you’ve jumped to conclusions and are misunderstanding his argument and making further assumptions based on your misunderstanding. Your argument is also ad-hominem by accusing people of believing things they don’t believe. Maybe it would be prudent to read some of the good critiques of Searle before trying to litigate it rapidly and sloppily on HN.
The randomness stuff is very straw man, definitely not a good argument, best to drop it. Today’s LLMs are deterministic, not random. Pseudorandom sequences come in different varieties, but they model some properties of randomness, not all of them. The functioning of today’s neural networks, both training and inference, is exactly a book of static rules, despite their use of pseudorandom sequences.
In case you missed it in the WP article, most of the field of cognitive science thinks Searle is wrong. However, they’re largely not critiquing him for using metaphysics, because that’s not his argument. He’s arguing that biology has mechanisms that binary electronic circuitry doesn’t; not human brains, simply physical chemical and biological processes. That much is certainly true. Whether there’s a difference in theory is unproven. But today currently there absolutely is a difference in practice, nobody has ever simulated the real world or a human brain using deterministic computation.
Nobody brings up that light travels through the aether, that diseases are caused by bad humors etc. - is it not right to call out people for stating theory that's believed to be false?
>The randomness stuff is very straw man,
And a direct response to what armada651 wrote:
>I think it's entirely valid to question whether a computer can form an understanding through deterministically processing instructions, whether that be through programming language or language training data.
> He’s arguing that biology has mechanisms that binary electronic circuitry doesn’t; not human brains, simply physical chemical and biological processes.
Once again the argument here changed from 'computers which only manipulate symbols cannot create consciousness' to 'we don't have the algorithm for consiousness yet'.
He might have successfully argued against the expert systems of his time - and true, mechanistic attempts at language translation have largely failed - but that doesn't extend to modern LLMs (and pre LLM AI) or even statistical methods.
Where did the argument change? Searle’s argument that you quoted is not arguing that we don’t have the algorithm yet. He’s arguing that the algorithm doesn’t run on electrical computers.
I’m not defending his argument, just pointing out that yours isn’t compelling because you don't seem to fully understand his, at least your restatement of it isn’t a good faith interpretation. Make his argument the strongest possible argument, and then show why it doesn’t work.
IMO modern LLMs don’t prove anything here. They don’t understand anything. LLMs aren’t evidence that computers can successfully think, they only prove that humans are prone to either anthropomorphic hyperbole, or to gullibility. That doesn’t mean computers can’t think, but I don’t think we’ve seen it yet, and I’m certainly not alone there.
>There’s no “scientific consensus” that he’s wrong, there are just opinions.
But the way Searle formulates his argument, by not defining what consciousness is, he essentially gives himself enough wiggle room to be always right - he's essentially making the 'No True Scotsman' fallacy.
That's one possibility. The other is that your pomposity and dismissiveness towards the entire field of philosophy speaks volumes on how little you know about either philosophical arguments in general or this philosophical argument in particular.
And yes, if for example, medicine would be no worse at curing cancer than it is today, yet doctors asserted that crystal healing is a serious study, that would reflect badly on the field at large, despite most of it being sound.
“Searle does not disagree with the notion that machines can have consciousness and understanding, because, as he writes, "we are precisely such machines". Searle holds that the brain is, in fact, a machine, but that the brain gives rise to consciousness and understanding using specific machinery.”
It's just a contradiction.
Even assuming a company gets to AGI first this doesn't mean another one will follow.
Suppose that FooAI gets to it first: - competitors may get there too in a different or more efficient way - Some FooAI staff can leave and found their own company - Some FooAI staff can join a competitor - FooAI "secret sauce" can be figured out, or simply stolen, by a competitor
At the end of the day, it really doesn't matter, the equation AI === commodity just does not change.
There is no way to make money by going into this never ending frontier model war, price of training keeps getting higher and higher, but your competitors few months later can achieve your own results for a fraction of your $.
There's a bunch of ways AI is improving itself, depending on how you want to interpret that. But it's been true since the start.
1. AI is used to train AI. RLHF uses this, curriculum learning is full of it, video model training pipelines are overflowing with it. AI gets used in pipelines to clean and upgrade training data a lot.
2. There are experimental AI agents that can patch their own code and explore a tree of possibilities to boost their own performance. However, at the moment they tap out after getting about as good as open source agents, but before they're as good as proprietary agents. There isn't exponential growth. There might be if you throw enough compute at it, but this tactic is very compute hungry. At current prices it's cheaper to pay an AI expert to implement your agent than use this.
Interesting. Do you have links?
AGI is a complete no go until a model can adjust its own weights on the fly, which requires some kind of negative feedback loop, which requires a means to determine a failure.
Humans have pain receptors to provide negative feedback and we can imagine events that would be painful such as driving into a parked car would be painful without having to experience it.
If current models could adjust its own weights to fix the famous “how many r’s in strawberry” then I would say we are on the right path.
However, the current solution is to detect the question and forward it to a function to determine the right answer. Or attempt to add more training data the next time the model is generated ($$$). Aka cheat the test.
[1] https://the-decoder.com/new-othello-experiment-supports-the-...
Mumbo jumbo magical thinking.
They perform so well because they are trained on probabilistic token matching.
Where they perform terribly, e.g math, reasoning, they are delegating to other approaches, and that's how you get the illusion that there is actually something there. But it's not. Faking intelligence is not intelligence. It's just text generation.
> In that sense, yeah you could say they are a bit "magical"
Nobody but the most unhinged hype pushers are calling it "magical". The LLM can never ever be AGI. Guessing the next word is not intelligence.
> there can be no form of world model that they are developing
Kind of impossible to form a world model if your foundation is probabilistic token guessing which is what LLMs are. LLMs are a dead end in achieving "intelligence", something novel as an approach needs to be discovered (or not) to go into the intelligence direction. But hey, at least we can generate text fast now!
There is no evidence to indicate this is the case. To the contrary, all evidence we have points to these models, over time, being able to perform a wider range of tasks at a higher rate of success. Whether it's GPQA, ARC-AGI or tool usage.
> they are delegating to other approaches > Faking intelligence is not intelligence. It's just text generation.
It seems like you know something about what intelligence actually is that you're not sharing. If it walks, talks and quacks like a duck, I have to assume it's a duck[1]. Though, maybe it quacks a bit weird.
Burden of proof is on those trying to convince us to buy into the idea of LLMs as being "intelligence".
There is no evidence of the Flying Spaghetti monster or Zeus or God not existing either, but we don't take seriously the people who claim they do exist (and there isn't proof because these concepts are made up).
Why should we take seriously the tolks claiming LLMs are intelligence without proof (there can't be proof, of course, because LLMs are not intelligence)?
If you're saying the magic disappeared after looking at a single transformer, did the magic of human intelligence disappear after you understood human neurons at a high level?
Are they still really hoping that they are gonna tweak a model and feed it an even bigger dataset and it will be AGI?
It is far from clear. There may well be emergent hierarchies of more abstract thought at much higher numbers of weights. We just don't know how a transformer will behave if one is built with 100T connections - something that would finally approach the connectome level of a human brain. Perhaps nothing interesting but we just do not know this and the current limitation in building such a beast is likely not software but hardware. At these scales the use of silicon transistors to approximate analog curve switching models just doesn't make sense. True neuromorphic chips may be needed to approach the numbers of weights necessary for general intelligence to emerge. I don't think there is anything in production at the moment that could rival the efficiency of biological neurons. Most likely we do not need that level of efficiency. But it's almost certain that stringing together a bunch of H100s isn't a path to the scale we should be aiming for.
So there probably isn’t even a legal moat.
AWS is also falling far behind Azure wrt serving AI needs at the frontier. GCP is also growing at a faster rate and has a way more promising future than AWS in this space.
From my admittely poorly informed point of view, strategy-wise, it's hard to tell how wise it is investing in foundational work at the moment. As long as some players release competitive open weight models, the competitive advantage of being a leader in R&D will be limited.
Amazon already has the compute power to place itself as a reseller without investing or having to share the revenue generated. Sure, they won't be at the forefront but they can still get their slice of the pie without exposing themselves too much to an eventual downturn.
Also a smart move is to be selling shovels in a gold rush - and that's exactly what Amazon is doing with AWS.
The blessing right now is the limit to contextual memory. Once those limits fall away and all of your previous conversations are made part of the context I suspect the game will change considerably, as will the players.
There's like a significant loss of model sharpness as context goes over 100K. Sometimes earlier, sometimes later. Even using context windows to their maximum extent today, the models are not always especially nuanced over the long ctx. I compact after 100K tokens.
Because my understandings is that, however you get to 100K, the 100,001st token is generated the same way as far as the model is concerned.
If you give a summary+graph to the model, it can still only attend to the summary for token 1. If it's going to call a tool for a deeper memory, it still only gets the summary when it makes the decision on what to call.
You get the same problem when asking the model to make changes in even medium-sized code bases. It starts from scratch each time, takes forever to read a bunch of files, and sometimes it reads the right stuff, other times it doesn't.
I dunno if this is possible; sounds like an informally specified ad-hoc statement of the halting problem.
Just look at the smartphone market.
how do you know memory won't be modular and avoid lock-in?
I can easily see a decentralized solution where the user owns the memory, and AIs need permission to access your data, which can be revoked.
Well, let’s take your life. Your life is about 3 billion seconds (100 year life). That’s just 3 billion next-tokens. The thing you do on second N is just, as a whole, a next token. If next-token prediction can be scaled up such that we redefine a token from a part of language to an entire discrete event or action, then it won’t be hard for the model to just know what you will think and do … next. Memory in that case is just the next possible recall of a specific memory, or next possible action, and so on. It doesn’t actually need all the memory information, it just needs to know that that you will seek a specific memory next.
Why would it need your entire database of memories if it already knows you will be looking for one exact memory next? The only thing that could explode the computational cost of this is if dynamic inputs fuck with your next token prediction. For example, you must now absolutely think about a Pink Elephant. But even that is constrained in our material world (still bounded physically, as the world can’t transfer that much information through your senses physically).
A human life up to this exact moment is just a series of tokens, believe it or not. We know it for a fact because we’re bounded by time. The thing you just thought was an entire world snapshot that’s no longer here, just like an LLM output. We have not yet trained a model on human lives yet, just knowledge.
We’re not done with the bitter lesson.
Why? It's just a bunch of text. They are forced by law to allow you to export your data - so you just take your life's "novel" and copy paste it into their competition's robot.
Ever since I started taking care of my LLM logs and memory, I had no issue switching model providers.
I think they learned some hard lessons from Alexa.
AWS has Bedrock to use various AI providers and has bundled the licensing into the price, so they are getting the users without having to develop the actual AI.
They provide the compute, networking etc, and they provide the users to the AI vendors.
Why would they need to develop their own?
The rambling answer to the “why are you behind” question on the last earnings call indicates it’s a sore spot for leadership, but at this point it’s too little too late. The best talent has already settled elsewhere. The only real saving grace is that if/when the AI bubble pops being so far behind might not be a terrible thing.
"Search is broken. If I search for wwvb watch, I get shown tons of watches which are definitely NOT WWVB."
"What browser are you using? Could you try Chrome?"
But actually every other company has been much more strategic, Microsoft is bullish because they partnered up with OpenAI and it pumps their share price to be bullish, Google is the natural home of a lot of this research.
But actually, Amazon, Apple etc aren't natural homes for this, they don't need to burn money to chase it.
So there we have it, the companies that have a good strategy for this are investing heavily, the others will pick up merges and key technological partners as the market matures, and presumably Zuck will go off and burn $XB on the next fad once AI has cooled down.
Google is leading in terms of fundamental technology, but not in terms of products
They had the LLambda chatbot before that, but I guess it was being de-emphasized, until ChatGPT came along
Social was a big pivot, though that wasn't really due to Pichai. That was while Larry Page was CEO and he argued for it hard. I can't say anyone could have known beforehand, but in retrospect, Google+ was poorly conceived and executed
---
I also believe the Nth Google chat app was based on WhatsApp success, but I can't remember the name now
Google Compute Engine was also following AWS success, after initially developling Google App Engine
They bought DeepMind in 2014 and always showed of a ton of AI research.
"AI" in it's current form is already a massive threat to Google's main business (I personally use Google only a fraction of what I used to), so this pivot is justified.
By more reasonable standards of "pivot", the big investment into Google Plus/Wave in the social media era seems to qualify. As does the billions spent building out Stadia's cloud gaming. Not to mention the billions invested in their abandoned VR efforts, and the ongoing investment into XR...
I'd personally define that as Google hedging their bet's and being prepared in case they needed to truly pivot, and then giving up when it became clear that they wouldn't need to. Sort of like "Apple Intelligence" but committing to the bit, and actually building something that was novel, and useful to some people, who were disappointed when it went away.
Stadia was always clearly unimportant to Google, and I say that as a Stadia owner (who got to play some games, and then got refunds.) As was well reported at the time, closing it was immaterial to their financials. Just because spending hundreds of millions of dollars or even a few billion dollars is significant to you or I doesn't mean that this was ever part of their core business.
Regardless, the overall sentimentality on HN about Google Reader and endless other indisputably small projects says more about the lack of strategic focus from people here, than it says anything about Alphabet.
Stadia was just Google's New Coke, Apple's Mac Cube, or Microsoft's MSNBC (or maybe Zune.
When they can't sell ads anymore, they'll have to pivot.
I mean, Facebook's core business hasn't actually failed yet either, but their massive investments in short-form video, VR/XR/Metaverse, blockchain, and AI are all because they see their moat crumbling and are desperately casting around for a new field to dominate.
Google feels pretty similar. They made a very successful gambit into streaming video, another into mobile, and a moderately successful one into cloud compute. Now the last half a dozen gambits have failed, and the end of the road is in sight for search revenue... so one of the next few investments better pay off (or else)
I didn't really see it at first, but I think you are correct to point out that they kind of rhyme. However to me, I think the clear desperation of Facebook makes it feel rather different from what I've seen Google doing over the years. I'm not sure I agree that Google's core business is in jeopardy in the way that Facebook's aging social media platform is.
A better way to look at it is that the absolute number 1 priority for google since they first created a money spiggot throguh monetising high-intent search and got the monopoly on it (outside of Amazon) has been to hold on to that. Even YT (the second biggest search engine on the internet other than google itself) is high intent search leading to advertising sales conversion.
So yes, google has adopted and killed lots of products, but for its big bets (web 2.0 / android / chrome) it's basically done everything it can to ensure it keeps it's insanely high revenue and margin search business going.
What it has to show for it is basically being the only company to have transitioned as dominent across technological eras (desktop -> web2.0 -> mobile -> (maybe llm).
As good as OpenAI is as a standalone, and as good as Claude / Claude Code is for developers, google has over 70% mobile market share with android, nearly 70% browser market share with chrome - this is a huge moat when it comes to integration.
You can also be very bullish about other possible trends. For AI - they are the only big provider which has a persistent hold on user data for training. Yes, OpenAI and Grok have a lot of their own data, but google has ALL gmail, high intent search queries, youtube videos and captions, etc.
And for AR/VR, android is a massive sleeping giant - no one will want to move wholesale into a Meta OS experience, and Apple are increasingly looking like they'll need to rely on google for high performance AI stuff.
All of this protects google's search business a lot.
Don't get me wrong, on the small stuff google is happy to let their people use 10% time to come up with a cool app which they'll kill after a couple of years, but for their big bets, every single time they've gone after something they have a lot to show for it where it counts to them.
The small stuff that they kill is just that--small stuff that was never important to them strategically.
I mean, sure, don't heavily invest (your attention, time, business focus, whatever) in something that is likely to be small to Google, unless you want to learn from their prototypes, while they do.
But to pretend that Google isn't capable of sustained intense strategic focus is to ignore what's clearly visible.
For Amazon “renting servers” at very high margin is their cash cow. For many competitors it’s more of a side business or something they’re willing to just take far lower margin on. Amazon needs to keep the markup high. Take away the AWS cash stream and the whole of Amazon’s financials start to look ugly. That’s likely driving the current panic with its leadership.
Culturally Amazon does really well when it’s an early mover leader in a space. It really struggles, and its leadership can’t navigate, when it’s behind in a sector as is playing out here.
Companies are not going to stop needing databases and the 307 other things AWS provides, no matter how good LLMs get.
Cheaper competitors have been trying to undercut AWS since the early days of its public availability, it has not worked to stop them at all. It's their very comprehensive offering, proven track record and the momentum that has shielded AWS and will continue to indefinitely.
Further AWS is losing share at a time when GCP and Azure are becoming profitable businesses, so no longer losing money to gain market share.
It's similar to how AWS became the de-facto cloud provider for newer companies. They struggled to convince existing Microsoft shops to migrate to AWS, instead most of the companies just migrated to Azure. If LLMs/AI become a major factor in new companies deciding which will be their default cloud provider, they're going to pick GCP or Azure.
LLMs look to be shaping up as an interchangeable commodity as training datasets, at least for general purpose use, converge to the limits of the available data, so access to customers seems just as important, if not more, than the models themselves. It seems it just takes money to build a SOTA LLM, but the cloud providers have more of a moat, so customer access is perhaps the harder part.
Amazon do of course have a close relationship with Anthropic both for training and serving models, which seems like a natural fit given the whole picture of who's in bed with who, especially as Anthropic and Amazon are both focused on business customers.
It doesn't have to be either/or of course - a cloud provider may well support a range of models, some developed in house and some not.
Vertical integration - a cloud provider building everything they sell - isn't necessarily the most logical business model. Sometimes it makes more sense to buy from a supplier, giving up a bit of margin, than build yourself.
I really liked the concept of Apple Intelligence with everything happening all on device, both process and data with minimal reliance off device to deliver the intelligence. It’s been disappointing that it hasn’t come to fruition yet. I am still hopeful the vapor materializes soon. Personally I wouldn’t mind seeing them burning a bit more to make it happen.
On the last earnings call the CEO gave a long rambling defensive response to an analyst question on why they’re behind. Reports from the inside also say that leaders are in full blown panic mode, pressing teams to come up with AI offerings even though Amazon really doesn’t have any recognized AI leaders in leadership roles and the best talent in tech is increasingly leaving or steering clear of Amazon.
I agree they should just focus on what they’re good at, which is logistics and fundamental “boring” compute infrastructure things. However leadership there though is just all over the map trying to convince folks their not behind vs just focusing on strengths.
They have huge exposure because of AWS; if the way people use computing shifts, and AWS isn't well-configured for AI workloads, then AWS has a lot to lose.
> Every other player is scrambling for hardware/electricity while Amazon has been building out data centers for the last 20 years.
Microsoft and Google have also been building out data centers for quite a while, but also haven't sat out the AI talent wars the way Amazon has.
What does that mean? Not enough GPUs?
1. Price-performance has struggled to stay competitive. There’s some supply-demand forces at play, but the top companies consistently seem to strike better deals elsewhere.
2. The way AWS is architected, especially on networking, isn’t ideal for AI. They’ve dug their heels on in their own networking protocols despite struggling to compete on performance. I personally know of several workloads that left AWS because they couldn’t compete on networking performance.
3. Struggling on the managed side. On paper a service like Bedrock should be great but in practice it’s been a hot mess. I’d love to use Anthropic via Bedrock, but it’s just much more reliable when going direct. AWS has never been great at these sort of managed services at scale and they’re again struggling here.
Zuckerberg failed every single fad he tried.
He's becoming more irrelevant every year and only the company's spoils from the past (earned not less by enabling, for example, a genocide to be committed in Myanmar https://www.pbs.org/newshour/world/amnesty-report-finds-face...) help carry them through to the series of disastrous idiotic decision Zuck is inflicting on them.
- VR with Oculus. It never caught on, for most people who own one, it's just gathering dust.
- Metaverse. They actually spend billions on that? https://www.youtube.com/watch?v=SAL2JZxpoGY
- LLAMA is absolute trash, a dumpster fire in the world of LLMs
Zuck is now trying to jump again on the LLM bandwagon and he's trying to...buy his way in with ridiculous pay packages: https://www.nytimes.com/2025/07/31/technology/ai-researchers.... Why is he so wrong to do that, you might ask?
He is doing it at the worst possible moment: LLMs are stagnating and even far better players than Meta like Anthropic and OpenAI can't produce anything worth writing about.
ChatGPT5 was a flop, Anthropic are struggling financially and are lowering token limits and preparing users for cranking up prices, going 180 on their promises not to use chat data for training, and Zuck, in his infinite wisdom, decides to hire top AI talent for premium price at a rapidly cooling market? You can't make up stuff like that.
It would appear that apart from being an ass kisser to Trump, Zuck shares another thing with the orange man-child running the US: a total inability to make good, or even sane deals. Fingers crossed that Meta goes bankrupt just like Trump's 6 banrkruptcies and then Zuck can focus on his MMA career.
I don't know in what circles you're hanging out, I don't know a single person who believed in the metaverse
Oh please, the world was full of hype journalists wanting to sound like they get it and they are in it, whatever next trash Facebook throws their way.
The same way folks nowadays pretend like the LLMs are the next coming of Jesus, it's the same hype as the scrum crowd, the same as crypto, nfts, web3. Always ass kissers who cant think for themselves and have to jump on some bandwagon to feign competence.
Look at what the idiots at Forbes wrote: https://www.forbes.com/councils/forbestechcouncil/2023/02/27...
They are still very influential, despite having shit takes loke that.
Accenture still think the Meta is groundbreaking: https://www.accenture.com/us-en/insights/metaverse
What a bunch of losers!
71% of executives seemed to be very excited about it: https://www.weforum.org/stories/2022/04/metaverse-will-be-go...
Executives (like Zuck) are famous for being rather stupid so if they are claiming something, you bet its not gonna happen.
Apparently, "The metaverse is slowly becoming the new generation’s digital engagement platform, but it’s making changes across enterprises, too."
https://www.softserveinc.com/en-us/blog/the-promise-of-the-m...
Go all in the new fad, investors pile up on your stock, dump, repeat...
Does he have this net worth because what he is doing or despite what he is doing? :-)
Correlation does not imply causation. Attribution is hard.
(The other 10% is mostly Google Maps and MercadoLibre.)
Buying competition is par for the course for near-monopolies in their niches. As long as the scale differences in value are still very large, you can avoid competition relatively cheaply, while the acquired still walk away with a lot of money.
This means there's two avenues:
1. Get a team of researchers to improve the quality of the models themselves to provide a _better_ chat interface
2. Get a lot of engineers to work LLMs into a useful product besides a chat interface.
I don't think that either of these options are going to pan out. For (1), the consumer market has been saturated. Laymen are already impressed enough by inference quality, there's little ground to be gained here besides a super AGI terminator Jarvis.
I think there's something to be had with agentic interfaces now and in the future, but they would need to have the same punching power to the public that GPT3 did when it came out to justify the billions in expenditure, which I don't think it will.
I think these companies might be able to break even if they can automate enough jobs, but... I'm not so sure.
I mean Cursor is already at $500 million ARR...
I could see the increased productivity of using Cursor indirectly generating a lot more value per engineer, but... I wouldn't put my money on it being worth it overall, and neither should investors chasing the Nvidia returns bag.
[1]: https://www.sec.gov/Archives/edgar/data/1326801/000132680114...
Being on the forefront of
(1) creating a personalized, per user data profile for ad-targeting is very much their core business. An LLM can do a very good job of synthesizing all the data they have on someone to try predicting things people will be interested in.
(2) by offering a free "ask me anything" service from meta.ai which is tied directly to their real-world human user account. They gather an even more robust user profile.
This isn't in-my-opinion simply throwing billions at a problem willy nilly. Figuring out how to apply this to their vast reams of existing customer data economically is going to directly impact their bottom line.
Is synthesizing the right word here?
Amazon is the biggest investor of AI of any company. They've already spent over $100b YTD on capex for AI infrastructure.
Microsoft's in a sweet spot. Apple's another interesting one, you can run local LLM models on your Mac really nicely. Are they going to outcompete an Nvidia GPU? Maybe not yet, but they're fast enough as-is.
Much more than the others, metter runs a content business. Gen AI aides in content generation so it behooves them to research it. Even before the current explosion of chatbots, meta was putting this stuff into their VR framework. It's used for their headset tracking and speech to text is helpful for controlling a headset without a physical keyboard.
You're making it sound like they'll follow anything that walks by but I do think it's more strategic than that.
Why wouldn't consumer AI be a natural home for Apple?
Apple is constantly under blast for being slow to AI but if you look at the current state of AI, it feels like something Apple would never release -- the quality just isn't there. I don't necessarily think Apple only dipping their toes into AI is that poor of a decision right now. They still have the ability to blow the roof off the market with agents and device integration whenever the tech is far enough along to be trustworthy to the average consumer.
So unless Apple thinks it can outcompete it's BigTech competitors in something it historically hasn't done much of, best leave it to them.
This sounds like you’re either unfamiliar with what software they make or underestimate the complexity of things like a modern operating system. For example, most people would consider Swift hard, or the various Core frameworks, or things like designing a new modern file system and doing in place migrations on billion devices, etc.
My bet is on Apple's upcoming announcement.
If only the technology existed to do work remotely, what a shame.
Metaverse will never be FaceBook.
Amazon though, sells physical goods and access to physical servers. Whatever is going on with AI, Amazon will profit from without having to burn its own money in advancing SOTA.
Why should they need to develop their own models?
HuwFulcher•2d ago